Platform Engineering Experts in Germany

maturing their API Practices on both, a Business and Technology level. My role encompasses strategy, architecture, developer advocacy as well as hands on software engineering, enabling both technical teams and business leadership to adopt and act on API- centric principles effectively. Coincidentally, we also establish GitOps, DX and Platform Best practices with this project.

Highlights:

• Aligning executives with the initiative by clarifying strategy, replacing misconceptions and myths with facts, clarifying the value of existing assets and enabling informed decision-making
• Formulating a way forward for API Lifecycle Management at OTTO
• Driving platform progress and fostering developer engagement by hands-on engineering work towards strategic goals

API Lifecycle Management, Team Topologies, Organizational Evolution, Regulatory, Platform Advocate, Developer Platform, Communities of Practice, Terraform, Kotlin, Kafka, Kong, WSO2, Apigee, Gravitee, Backstage, AsyncAPI, OpenAPI, API Design, AWS, react, nodejs, typescript, redocly, reactive programming, CDC, golang, gingonic, GitOps, DX (developer experience), stakeholder management, roadmaps, workshops, discovery.

• Designed and developed the architecture of an enterprise SCM/CI platform for Kubernetes-native delivery and GitOps workflows.
• Implemented infrastructure automation and Vault & IAM integration for secure, compliant pipelines.
• Coordinated cross-functional teams to improve DevOps, security, and architecture in release processes.
• Increased platform adoption and developer experience by automating onboarding and artifact pipelines.

Manufacturing intelligence platform with enterprise workflows, RAG, real-time AI assistant features, and multi-repository backend architecture.

• Built and extended production AI/backend services with Django, DRF, FastAPI, GraphQL, Celery, PostgreSQL, MySQL, Redis, and WebSockets across a modular multi-repository platform.
• Contributed to ARIS V2, a real-time manufacturing AI assistant using LangChain, LangGraph, MCP tool orchestration, planning/execution flows, OpenAI, AWS Bedrock, Qdrant, and Elasticsearch/OpenSearch-backed retrieval.
• Supported rollout expansion from ARIS V1 in 4 of 17 client production plants to ARIS V2 currently active in 13 of 17 plants, increasing real-world deployment coverage to more than 50% of the client footprint.
• Worked on document-grounded RAG functionality including ingestion, OCR, chunking, embeddings, indexing, retrieval, reranking, and grounded answer generation for industrial workflows.

Stack: Python, Django, DRF, FastAPI, LangChain, LangGraph, GraphQL, Celery, WebSockets, OpenAI, AWS Bedrock, Qdrant, Elasticsearch/OpenSearch, PostgreSQL, MySQL, Redis, Docker.

• Led migration to GCP using Terraform, GKE, and GitOps, improving deployment consistency and scalability
• Implemented Datadog observability stack via Terraform and datadog-operator
• Established automated end-to-end tests and on-call processes, improving incident response and service reliability
• Migrated from NGINX Ingress Controller to Kubernetes Gateway API (NGINX Gateway Fabric)
• Migrated stateful services (PostgreSQL and Redis) to GCP, improving scalability and operational reliability

• Developed an evaluation framework for AI-generated multiview driving videos intended for perception and embodied-AI/VLA-related training workflows.
• Designed automated checks for temporal coherence, cross-camera consistency, semantic correctness, and multiview geometric quality, exposing failure modes relevant to autonomous systems.
• Combined classical computer vision, learned visual representations, and vision-language models to convert complex video artifacts into measurable engineering signals.
• Built repeatable benchmarking and failure-analysis workflows to support model comparison, data-quality decisions, and system-improvement discussions.

• Responsible for the architecture and development of an AI-native SaaS platform for industrial product portfolio management.
• Designed the modern data platform architecture on Azure for scalable analytics and enterprise data integration.
• Built enterprise data ingestion and transformation pipelines across complex industrial system landscapes.
• Developed graph-based representations of product structures and dependencies for analytical reasoning.
• Designed and implemented an agentic AI framework for AI-supported decision workflows.
• Built scalable analytical microservices and integrated reporting through modern BI technologies.
• Coordinated backend, AI, and frontend development across the MYNR platform stack.

• Designed and implemented a hybrid recommendation system (content-based + collaborative filtering)
• Built end-to-end ML pipelines including data processing, feature engineering, model training, and evaluation
• Developed RAG-based LLM systems using LangChain and vector databases for semantic search and knowledge retrieval
• Established MLOps workflows with MLflow for experiment tracking, versioning, and deployment readiness
• Implemented deep learning models (computer vision & classification) using PyTorch and TensorFlow

• Held direct line management responsibility for a team of 4 Platform Engineers — owning hiring, performance reviews, and career development — while establishing a shared engineering standards framework and coaching culture that accelerated delivery across client engagements.
• Led a team of engineers to architect a cloud-native voice AI system for a major inspection client, enabling 2,500 field inspectors to document work fully hands-free via real-time transcription and AI agents — eliminating manual data entry across 440,000 inspections per month and reducing per-user cost from $9 to $1. Stack: AWS (DynamoDB, S3, Transcribe, CloudFront, API Gateway, Bedrock), ElevenLabs, Claude.
• Led a team of engineers to automate multi-region Kubernetes cluster management for a global SaaS leader, reducing provisioning time from 3 weeks to under a day and eliminating 90% of configuration errors. Stack: EKS, Terragrunt, Python, Bash, ArgoCD.
• Accelerator - Cloud-Agnostic AI Platform: Architected and delivered a cloud-agnostic, Kubernetes-native platform as an accelerator, enabling multi-tenant, enterprise-scale management of self-hosted LLMs with concurrent deployment of multiple base models and dynamic LoRA adapter serving. Designed production infrastructure using open-source tooling (ArgoCD, Karpenter, vLLM, SGLang) with automated model lifecycle management, API security (Keycloak + LiteLLM), and cost-optimized GPU provisioning.

• Built an enterprise data lakehouse platform on Azure Databricks

• Developed production data pipelines and governance structures

• Implemented private cloud infrastructures using Terraform

• Introduced modern CI/CD standards in Azure DevOps

• Implemented secure IAM and governance concepts

• Developed scalable PySpark and Delta Lake frameworks

• Supported self-service analytics and data product approaches

• Provided architecture and platform consulting for enterprise data initiatives

• Built a central DataHub architecture for insurance data

• Integrated multiple subsystems into a lakehouse platform

• Introduced data governance and data lineage

• Supported modern analytics and reporting standards

• Optimized data delivery for business and analytics teams

• Bedrock technology operations role providing the foundations on which PUMA's global product teams build PUMA.com and associated systems
• Managed a €3.5 million CAPEX portfolio including ~20 externals and 16 platform and tooling vendors
• Started up and led five functional teams for platform engineering, cloud administration, edge technology, continuous performance testing, and L2 support / incident management
• Person of last resort for mysteries, intractable bugs, compliance crises, litigation support, and black-hole issues where ownership was unclear or contested
• Sole author and driver of a multi-million-euro, multi-year RFP for Global Operations Center serving the direct-to-consumer technology estate for eCommerce, order management, and brick-and-mortar retail tech support
• Conceived, designed, developed and productionized Order Viewer, a secure internal application providing observability into PUMA.com order data at sub-second latency; independently delivered architecture, implementation, GCP deployment, SSO integration and organizational compliance.

• Developed and implemented organization-wide discovery framework based on Ulwick’s Outcome-Driven Innovation; enabled 7 Product Owners to systematically identify and quantify unrealized value through shared outcome language and opportunity scoring methodology
• Transformed Product Owner role from backlog clerks to strategic experimenters; established dedicated time budget for autonomous hypothesis testing and discovery activities
• Rebuilt customer journey maps to start at actual user need (tool selection phase) instead of platform entry point; eliminated manual data aggregation work previously done by project teams
• Implemented OKR framework across 4 product teams; defined quarterly objectives with measurable key results (e.g., 40% reduction in manual integration effort, self-service adoption increase)
• Unified 3 separate platform roadmaps through cross-team dependency mapping and shared service agreements
• Supported enterprise sales cycle with ROI modeling and technical due diligence for automotive and defense customers

• Cloud Computing Fundamentals & Architecture: Expertise in core cloud concepts, including the three main Service Models (IaaS, PaaS, SaaS) and diverse Deployment Models (Public, Private, Hybrid, Multi-cloud).
• Modern Application Deployment Strategies (GCP Focus): Instruction on the GCP Application Hosting Spectrum, covering Virtual Machines, Containers (Kubernetes and Cloud Run), Platform as a Service (App Engine), and Serverless Computing (Functions as a Service - FaaS).
• Data Management & Big Data Analytics: Comprehensive coverage of Cloud Storage options (Object, Block, File) and Database solutions, including Relational (Cloud SQL), NoSQL (Firestore, BigTable, Memorystore), and serverless enterprise data warehousing (BigQuery).
• DevOps and Infrastructure Automation: Skills in DevOps principles, including Continuous Integration (CI), Continuous Delivery (CD), Infrastructure as Code (IaC) using tools like Terraform, and implementing effective Monitoring and Logging for system observability.
• Emerging Technologies & Responsible Cloud Use: Focus on crucial topics like Cloud and IoT Security, Identity and Access Management (IAM), data privacy, and the ethical considerations of cloud and massive data collection.

• Requirements analysis for the enterprise-wide observability platform.
• Creation of playbooks and pipelines for rolling out Envoy, OpenTelemetry Collectors, and OpenTelemetry Agents.
• Conducting load tests for capacity planning of metrics for the observability platform.
• Documentation and implementation of compliance standards for production readiness.
• Creation and design of RED metrics, spanmetrics, JBoss, and Tomcat dashboards for mission-critical applications.
• Setting up alerts for critical applications for proactive incident response.
• Integration of OpenShift applications into the enterprise-wide observability stack.

• Led multiple concurrent, enterprise-scale technical programs spanning backend services, APIs, identity platforms, cloud infrastructure, and security
• Actively participated in system architecture and design reviews, validating service interactions, data flows, security models, and scalability requirements
• Worked hands-on with engineering teams to decompose complex initiatives into technical epics, stories, and deliverables
• Reviewed technical approaches, migration strategies, and rollout plans to minimize operational and security risk
• Delivered SSO, MFA, directory services, SaaS integrations, and platform modernization impacting tens of thousands of users globally
• Drove Agile delivery across globally distributed teams; facilitated ceremonies, resolved blockers, and improved engineering throughput
• Established technical RAID logs, dependency maps, and milestone-based execution plans in regulated enterprise environments
• Acted as a trusted technical partner to senior leadership, translating complex engineering topics into business-relevant outcomes